This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover stochastic differential equations, the concentration here on inference makes this book stand out. No other direct competitors are known to date. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will...
This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The int...
Stochastic di?erential equations model stochastic evolution as time evolves. These models have a variety of applications in many disciplines and emerge naturally in the study of many phenomena. Examples of these applications are physics (see, e. g., 176] for a review), astronomy 202], mechanics 147], economics 26], mathematical ?nance 115], geology 69], genetic analysis (see, e. g., 110], 132], and 155]), ecology 111], cognitive psychology (see, e. g., 102], and 221]), neurology 109], biology 194], biomedical sciences 20], epidemi- ogy 17], political analysis and social...
Stochastic di?erential equations model stochastic evolution as time evolves. These models have a variety of applications in many disciplines and emerg...
Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous time processes and numerical option pricing. Introduces the bases of probability theory and goes on to explain how to model financial times series with continuous models, how to calibrate them from discrete data and further covers option pricing with one or more underlying assets based on these models.
Analysis and implementation of models goes beyond the standard Black and Scholes framework and includes Markov switching models, Levy models and other...
Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous time proce...
The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Levy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical...
The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential eq...
